{"id":895428,"date":"2022-11-08T09:00:00","date_gmt":"2022-11-08T17:00:00","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/?p=895428"},"modified":"2022-11-08T06:23:21","modified_gmt":"2022-11-08T14:23:21","slug":"research-focus-week-of-november-7-2022","status":"publish","type":"post","link":"https:\/\/www.microsoft.com\/en-us\/research\/blog\/research-focus-week-of-november-7-2022\/","title":{"rendered":"Research Focus: Week of November 7, 2022"},"content":{"rendered":"\n<figure class=\"wp-block-image size-full\"><img loading=\"lazy\" decoding=\"async\" width=\"2560\" height=\"483\" src=\"https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2022\/11\/1400x264_Research_focus3_blog_hero-scaled.jpg\" alt=\"Microsoft Research Focus 03: Week of November 7th, 2022\" class=\"wp-image-896196\" srcset=\"https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2022\/11\/1400x264_Research_focus3_blog_hero-scaled.jpg 2560w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2022\/11\/1400x264_Research_focus3_blog_hero-300x57.jpg 300w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2022\/11\/1400x264_Research_focus3_blog_hero-1024x193.jpg 1024w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2022\/11\/1400x264_Research_focus3_blog_hero-768x145.jpg 768w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2022\/11\/1400x264_Research_focus3_blog_hero-1536x290.jpg 1536w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2022\/11\/1400x264_Research_focus3_blog_hero-2048x386.jpg 2048w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2022\/11\/1400x264_Research_focus3_blog_hero-240x45.jpg 240w\" sizes=\"(max-width: 2560px) 100vw, 2560px\" \/><\/figure>\n\n\n\n<figure class=\"wp-block-pullquote\"><blockquote><p><em class=\"\">Welcome to Research Focus, a new series of blog posts that highlights notable publications, events, code\/datasets, new hires and other milestones from across the research community at Microsoft.<\/em><\/p><\/blockquote><\/figure>\n\n\n<aside id=accordion-236e1f0e-d2f8-44c7-a9af-b99150a84536 class=\"msr-table-of-contents-block accordion mb-5 pb-0\" data-bi-aN=\"table-of-contents\">\n\t<button class=\"btn btn-collapse bg-gray-100 mb-0 display-flex justify-content-between\" type=\"button\" data-mount=\"collapse\" data-target=\"#accordion-collapse-236e1f0e-d2f8-44c7-a9af-b99150a84536\" aria-expanded=\"true\" aria-controls=\"accordion-collapse-236e1f0e-d2f8-44c7-a9af-b99150a84536\">\n\t\t<span class=\"msr-table-of-contents-block__label subtitle\">In this article<\/span>\n\t\t<span class=\"msr-table-of-contents-block__current mr-4 text-gray-600 font-weight-normal\" aria-hidden=\"true\"><\/span>\n\t<\/button>\n\t<div id=\"accordion-collapse-236e1f0e-d2f8-44c7-a9af-b99150a84536\" class=\"msr-table-of-contents-block__collapse-wrapper collapse show\" data-parent=\"#accordion-236e1f0e-d2f8-44c7-a9af-b99150a84536\">\n\t\t<div class=\"accordion-body bg-gray-100 border-top pt-4\">\n\t\t\t<ol class=\"msr-table-of-contents-block__list\">\n\t\t\t\t\t\t\t\t\t<li class=\"msr-table-of-contents-block__list-item\">\n\t\t\t\t\t\t<a href=\"#microsoft-turing-universal-language-representation-model-t-ulrv6-tops-both-xtreme-and-glue-leaderboards-with-a-single-model\" class=\"msr-table-of-contents-block__list-item-link\">Microsoft Turing Universal Language Representation model, T-ULRv6, tops both XTREME and GLUE leaderboards with a single model<\/a>\n\t\t\t\t\t<\/li>\n\t\t\t\t\t\t\t\t\t<li class=\"msr-table-of-contents-block__list-item\">\n\t\t\t\t\t\t<a href=\"#pact-perception-action-causal-transformer-for-autoregressive-robotics-pretraining\" class=\"msr-table-of-contents-block__list-item-link\">PACT: Perception-Action Causal Transformer for autoregressive robotics pretraining<\/a>\n\t\t\t\t\t<\/li>\n\t\t\t\t\t\t\t\t\t<li class=\"msr-table-of-contents-block__list-item\">\n\t\t\t\t\t\t<a href=\"#microsoft-research-and-nhs-scotland-conduct-worlds-first-clinical-trials-of-holoportation-based-3d-telemedicine-system-to-increase-access-to-healthcare\" class=\"msr-table-of-contents-block__list-item-link\">Microsoft Research and\u00a0NHS Scotland\u00a0conduct world\u2019s first clinical trials of\u00a0Holoportation\u2122-based 3D telemedicine system to increase access to healthcare<\/a>\n\t\t\t\t\t<\/li>\n\t\t\t\t\t\t\t\t\t<li class=\"msr-table-of-contents-block__list-item\">\n\t\t\t\t\t\t<a href=\"#interactive-code-generation-via-test-driven-user-intent-formalization\" class=\"msr-table-of-contents-block__list-item-link\">Interactive code generation via test-driven user-intent formalization<\/a>\n\t\t\t\t\t<\/li>\n\t\t\t\t\t\t\t<\/ul>\n\t\t<\/div>\n\t<\/div>\n\t<span class=\"msr-table-of-contents-block__progress-bar\"><\/span>\n<\/aside>\n\n\n\n<h2 id=\"microsoft-turing-universal-language-representation-model-t-ulrv6-tops-both-xtreme-and-glue-leaderboards-with-a-single-model\">Microsoft Turing Universal Language Representation model, T-ULRv6, tops both XTREME and GLUE leaderboards with a single model<\/h2>\n\n\n\n<p><em><a href=\"https:\/\/www.microsoft.com\/en-us\/research\/people\/bapatra\/\" target=\"_blank\" rel=\"noreferrer noopener\">Barun Patra<\/a>, Saksham Singhal, <a href=\"https:\/\/www.microsoft.com\/en-us\/research\/people\/shaohanh\/\" target=\"_blank\" rel=\"noreferrer noopener\">Shaohan Huang<\/a>, Zewen Chi, <a href=\"https:\/\/www.microsoft.com\/en-us\/research\/people\/lidong1\/\" target=\"_blank\" rel=\"noreferrer noopener\">Li Dong<\/a>, <a href=\"https:\/\/www.microsoft.com\/en-us\/research\/people\/fuwei\/\" target=\"_blank\" rel=\"noreferrer noopener\">Furu Wei<\/a>, <a href=\"https:\/\/www.microsoft.com\/en-us\/research\/people\/vchaudhary\/\" target=\"_blank\" rel=\"noreferrer noopener\">Vishrav Chaudhary<\/a>, <a href=\"https:\/\/www.microsoft.com\/en-us\/research\/people\/xiaso\/\" target=\"_blank\" rel=\"noreferrer noopener\">Xia Song<\/a><\/em><\/p>\n\n\n\n<p>The most recent addition to Microsoft&#8217;s Turing Universal Language Representation Model family (T-ULRv6) has achieved the top position on both the Google <a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" href=\"https:\/\/sites.research.google\/xtreme\" target=\"_blank\" rel=\"noreferrer noopener\">XTREME<\/a> and <a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" href=\"https:\/\/gluebenchmark.com\/\" target=\"_blank\" rel=\"noreferrer noopener\">GLUE<\/a> leaderboards, the first time that a single multilingual model has demonstrated state-of-the-art capabilities in both English and multilingual understanding tasks. The result of a collaboration between the <a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" href=\"https:\/\/turing.microsoft.com\/\" target=\"_blank\" rel=\"noreferrer noopener\">Microsoft Turing<\/a> team and <a href=\"https:\/\/www.microsoft.com\/en-us\/research\/\" target=\"_blank\" rel=\"noreferrer noopener\">Microsoft Research<\/a>, the T-ULRv6 XXL model is based on \u201c<a href=\"https:\/\/www.microsoft.com\/en-us\/research\/publication\/beyond-english-centric-bitexts-for-better-multilingual-language-representation-learning\/\" target=\"_blank\" rel=\"noreferrer noopener\">XY-LENT<\/a>,\u201d which leverages X-Y (non-English Centric) bitexts and incorporates the key innovations of XLM-E, the improved training data and vocabulary, and the advanced fine-tuning technique of <a href=\"https:\/\/www.microsoft.com\/en-us\/research\/publication\/consistency-regularization-for-cross-lingual-fine-tuning\/\" target=\"_blank\" rel=\"noreferrer noopener\">XTune<\/a>. Furthermore, to enable scaling to XXL sized models, T-ULRv6 leverages the memory optimization benefits afforded by <a href=\"https:\/\/www.microsoft.com\/en-us\/research\/publication\/zero-memory-optimizations-toward-training-trillion-parameter-models\/\" target=\"_blank\" rel=\"noreferrer noopener\">ZeRO<\/a>. To effectively utilize X-Y bitexts, the team adopted a novel sampling strategy and reconstructed the vocabulary using <a href=\"https:\/\/www.microsoft.com\/en-us\/research\/publication\/allocating-large-vocabulary-capacity-for-cross-lingual-language-model-pre-training\/\" target=\"_blank\" rel=\"noreferrer noopener\">VoCap<\/a>, which ensures an efficient distribution of data across languages and helps mitigate sparse sampling distributions from previous works. The XXL model variant outperforms both XLM-R XXL and mT5 XXL while being ~2x and ~3x smaller, respectively. <br><br>T-ULRv6 powers the language universalization of Microsoft Bing, enabling users to search and discover information across languages and domains. T-ULRv6 will soon enhance other Microsoft products with its multilingual capabilities.&nbsp;<\/p>\n\n\n\n<p>XTREME, or Cross-lingual TRansfer Evaluation of Multilingual Encoders, is a benchmark covering 40 typologically diverse languages across 12 language families, with nine tasks that require reasoning about syntax or semantics.<\/p>\n\n\n\n<p>GLUE \u2013 or the General Language Understanding Evaluation benchmark \u2013 is a collection of resources for training, evaluating, and analyzing natural language understanding systems.<\/p>\n\n\n<section class=\"carousel-item msr-cards__card msr-cards__card--carousel\" aria-label=\"Slide 1 of 0\" aria-roledescription=\"slide\">\n\n\t<div class=\"card material-card h-100 p-0\" data-mount=\"click-group\">\n\n\t\t\n\t\t<div class=\"card-body px-4 px-lg-5 pt-4\">\n\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t<\/div>\n<\/section>\n\n\n\n<div class=\"wp-block-buttons is-content-justification-center is-content-justification-center is-layout-flex wp-container-core-buttons-is-layout-1 wp-block-buttons-is-layout-flex\">\n<div class=\"wp-block-button\"><a data-bi-type=\"button\" class=\"wp-block-button__link\" href=\"https:\/\/blogs.bing.com\/search-quality-insights\/october-2022\/Microsoft-Turing-Universal-Language-Representation-model,-T-ULRv6,-tops-both-XTREME-and-GLUE-leaderb\" target=\"_blank\" rel=\"noreferrer noopener\">Read the blog<\/a><\/div>\n\n\n\n<div class=\"wp-block-button is-style-outline is-style-outline--39b642ef20ab1e6a3b4be376f9c5b842\"><a data-bi-type=\"button\" class=\"wp-block-button__link\" href=\"https:\/\/www.microsoft.com\/en-us\/research\/publication\/beyond-english-centric-bitexts-for-better-multilingual-language-representation-learning\/\" target=\"_blank\" rel=\"noreferrer noopener\">Read the paper<\/a><\/div>\n<\/div>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity is-style-dots\"\/>\n\n\n\n<h2 id=\"pact-perception-action-causal-transformer-for-autoregressive-robotics-pretraining\">PACT: Perception-Action Causal Transformer for autoregressive robotics pretraining<\/h2>\n\n\n\n<p><em><a href=\"https:\/\/www.microsoft.com\/en-us\/research\/people\/rbonatti\/\" target=\"_blank\" rel=\"noreferrer noopener\">Rogerio Bonatti<\/a>, <a href=\"https:\/\/www.microsoft.com\/en-us\/research\/people\/savempra\/\" target=\"_blank\" rel=\"noreferrer noopener\">Sai Vemprala<\/a>, <a href=\"https:\/\/www.microsoft.com\/en-us\/research\/people\/shuama\/\" target=\"_blank\" rel=\"noreferrer noopener\">Shuang Ma<\/a>, <a href=\"https:\/\/www.microsoft.com\/en-us\/research\/people\/fevieira\/\" target=\"_blank\" rel=\"noreferrer noopener\">Felipe Frujeri<\/a>, Shuhang Chen, <a href=\"https:\/\/www.microsoft.com\/en-us\/research\/people\/akapoor\/\" target=\"_blank\" rel=\"noreferrer noopener\">Ashish Kapoor<\/a><\/em><\/p>\n\n\n\n<p>Recent advances in machine learning architectures have induced a paradigm shift from task-specific models towards large general-purpose networks. For instance, in the past few years we have witnessed a revolution in the domains of natural language and computer vision with models such as GPT-3, BERT and DALL-E. The field of robotics is still mostly riddled with single-purpose systems architectures whose modules and connections, whether traditional or learning-based, require significant human design expertise. Inspired by these large pre-trained models, this work introduces a general-purpose robotics representation that can serve as a starting point for multiple tasks for a mobile agent, such as navigation, mapping and localization.<\/p>\n\n\n\n<p>We present the <a href=\"https:\/\/www.microsoft.com\/en-us\/research\/group\/autonomous-systems-group-robotics\/articles\/perception-action-causal-transformer-for-autoregressive-robotics-pretraining\/\" target=\"_blank\" rel=\"noreferrer noopener\">Perception-Action Causal Transformer<\/a> (PACT), a generative transformer-based architecture that aims to build representations directly from robot data in a self-supervised fashion. Through autoregressive prediction of states and actions over time, our model implicitly encodes dynamics and behaviors for a particular robot. This representation can then function as a single starting point to achieve distinct tasks through fine-tuning with minimal data.<\/p>\n\n\n\n<div class=\"wp-block-buttons is-content-justification-center is-content-justification-center is-layout-flex wp-container-core-buttons-is-layout-2 wp-block-buttons-is-layout-flex\">\n<div class=\"wp-block-button is-style-fill-github\"><a data-bi-type=\"button\" class=\"wp-block-button__link\" href=\"https:\/\/github.com\/microsoft\/PACT\" target=\"_blank\" rel=\"noreferrer noopener\">Download<\/a><\/div>\n\n\n\n<div class=\"wp-block-button is-style-outline is-style-outline--230b440b1617d52a075eac1a8e1906ff\"><a data-bi-type=\"button\" class=\"wp-block-button__link\" href=\"https:\/\/www.microsoft.com\/en-us\/research\/publication\/pact-perception-action-causal-transformer-for-autoregressive-robotics-pre-training\/\" target=\"_blank\" rel=\"noreferrer noopener\">Read the paper<\/a><\/div>\n<\/div>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity is-style-dots\"\/>\n\n\n\n<h2 id=\"microsoft-research-and-nhs-scotland-conduct-worlds-first-clinical-trials-of-holoportation-based-3d-telemedicine-system-to-increase-access-to-healthcare\">Microsoft Research and&nbsp;NHS Scotland&nbsp;conduct world\u2019s first clinical trials of&nbsp;Holoportation\u2122-based 3D telemedicine system to increase access to healthcare<\/h2>\n\n\n\n<p><em>Steven Lo, <a href=\"https:\/\/www.microsoft.com\/en-us\/research\/people\/sfowers\/\" target=\"_blank\" rel=\"noreferrer noopener\">Spencer Fowers<\/a>, Kwame Darko, <a href=\"https:\/\/www.microsoft.com\/en-us\/research\/people\/tvallinspina\/\" target=\"_blank\" rel=\"noreferrer noopener\">Thiago Spina<\/a>, Catriona Graham, <a href=\"https:\/\/www.microsoft.com\/en-us\/research\/people\/andreabri\/\" target=\"_blank\" rel=\"noreferrer noopener\">Andrea Britto<\/a>, Anna Rose, <a href=\"https:\/\/www.microsoft.com\/en-us\/research\/people\/datittsw\/\" target=\"_blank\" rel=\"noreferrer noopener\">David Tittsworth<\/a>, Aileen McIntyre, Chris O&#8217;Dowd, Roma Maguire, Wayne Chang, David Young, <a href=\"https:\/\/www.microsoft.com\/en-us\/research\/people\/amhoak\/\" target=\"_blank\" rel=\"noreferrer noopener\">Amber Hoak<\/a>, Robin Young, Mark Dunlop, Levi Ankrah, Martina Messow, Opoku Ampomah, Ben Cutler, Roma Armstrong, Ruchi Lalwani, Ruairidh Davison, Sophie Bagnall, Whitney Hudson, <a href=\"https:\/\/www.microsoft.com\/en-us\/research\/people\/mikeshep\/\" target=\"_blank\" rel=\"noreferrer noopener\">Mike Shepperd<\/a>, Jonny Johnson, 3DTM (3D Telemedicine) Collaborative research group<\/em><\/p>\n\n\n\n<p>The Covid pandemic has increased the usage of remote health consultations and underscored the need for a better system. Current 2D telemedicine engagements fail to replicate the fluency or authenticity of in-person consultations. Real-time 3D telemedicine has previously been proposed within a research setting only, with constraints on complexity, bandwidth and technology.<\/p>\n\n\n\n<p>This research reports on an international collaboration on the participatory development and first validated clinical use of a novel, real-time 360-degree 3D telemedicine system worldwide. NHS Greater Glasgow and Clyde have been working with Microsoft since 2019 to assess how health care could leverage Microsoft\u2019s 3D telemedicine, focusing on plastic surgery patients and leveraging Microsoft&#8217;s Holoportation\u2122\u202fcommunication technology.<\/p>\n\n\n\n<p>This research was designed to compare validated outcome measures of a patient-centered 3D telemedicine system with a 2D system, assess alignment with an in-person consultation, and to ensure safety, reliability and clinical concordance. In three separate studies, the 3D system improved patient metrics in comparison to 2D telemedicine, suggesting that remote consultations could get closer to the experience of face-to-face consultations.<\/p>\n\n\n\n<div class=\"wp-block-buttons is-content-justification-center is-content-justification-center is-layout-flex wp-container-core-buttons-is-layout-3 wp-block-buttons-is-layout-flex\">\n<div class=\"wp-block-button is-style-outline is-style-outline--4acf592c409848650f7035ee3db8db0a\"><a data-bi-type=\"button\" class=\"wp-block-button__link\" href=\"https:\/\/www.microsoft.com\/en-us\/research\/publication\/participatory-development-of-a-3d-telemedicine-system-during-covid-the-future-of-remote-consultations\/\" target=\"_blank\" rel=\"noreferrer noopener\">Read the paper<\/a><\/div>\n<\/div>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity is-style-dots\"\/>\n\n\n\n<h2 id=\"interactive-code-generation-via-test-driven-user-intent-formalization\">Interactive code generation via test-driven user-intent formalization<\/h2>\n\n\n\n<p><em><a href=\"https:\/\/www.microsoft.com\/en-us\/research\/people\/shuvendu\/\" target=\"_blank\" rel=\"noreferrer noopener\">Shuvendu Lahiri<\/a>, Aaditya Naik, Georgios Sakkas, <a href=\"https:\/\/www.microsoft.com\/en-us\/research\/people\/pialic\/\" target=\"_blank\" rel=\"noreferrer noopener\">Piali Choudhury<\/a>, <a href=\"https:\/\/www.microsoft.com\/en-us\/research\/people\/curtisvv\/\" target=\"_blank\" rel=\"noreferrer noopener\">Curtis von Veh<\/a>, <a href=\"https:\/\/www.microsoft.com\/en-us\/research\/people\/madanm\/\" target=\"_blank\" rel=\"noreferrer noopener\">Madan Musuvathi<\/a>, <a href=\"https:\/\/www.microsoft.com\/en-us\/research\/people\/jinala\/\" target=\"_blank\" rel=\"noreferrer noopener\">Jeevana Priya Inala<\/a>, <a href=\"https:\/\/www.microsoft.com\/en-us\/research\/people\/chenwang\/\" target=\"_blank\" rel=\"noreferrer noopener\">Chenglong Wang<\/a>, <a href=\"https:\/\/www.microsoft.com\/en-us\/research\/people\/jfgao\/\" target=\"_blank\" rel=\"noreferrer noopener\">Jianfeng Gao<\/a><\/em><\/p>\n\n\n\n<p>Automatic code generation from natural language intent using large language models is disrupting coding. However, the correctness of the resulting code with respect to user intent expressed in natural language is difficult to establish because natural language lacks formal semantics. In this project, we investigate the problem of <em>neural specification generation<\/em> (i.e., generating partial formal specifications that match the intent expressed in natural language), and incorporating such specifications during the coding process to improve trust in human-written or AI-generated code.<\/p>\n\n\n\n<p>We instantiate this framework starting with <em>unit tests;<\/em> tests serve as weak yet formal specifications of a module. We can leverage the abundance of human-written unit tests to train models. Further, these specifications (tests) can be checked using concrete execution without the need for more sophisticated abstract interpreters.&nbsp; In prior work on <a href=\"https:\/\/www.microsoft.com\/en-us\/research\/publication\/toga-a-neural-method-for-test-oracle-generation\/\" target=\"_blank\" rel=\"noreferrer noopener\">TOGA<\/a>, we demonstrated a neural model for <em>synthesizing test oracles<\/em> for a method and illustrated its use in finding functional bugs in code. In this work on <a href=\"https:\/\/www.microsoft.com\/en-us\/research\/publication\/interactive-code-generation-via-test-driven-user-intent-formalization\/\" target=\"_blank\" rel=\"noreferrer noopener\">TiCoder<\/a>, we describe an interactive workflow to formalizing the informal user-intent through such model-generated tests and improving the accuracy, correctness and understanding of generated code.<\/p>\n\n\n\n<div class=\"wp-block-buttons is-content-justification-center is-content-justification-center is-layout-flex wp-container-core-buttons-is-layout-4 wp-block-buttons-is-layout-flex\">\n<div class=\"wp-block-button is-style-outline is-style-outline--c15caf0bd2c625209994c759793b468d\"><a data-bi-type=\"button\" class=\"wp-block-button__link\" href=\"https:\/\/www.microsoft.com\/en-us\/research\/publication\/interactive-code-generation-via-test-driven-user-intent-formalization\/\" target=\"_blank\" rel=\"noreferrer noopener\">Read the paper<\/a><\/div>\n<\/div>\n","protected":false},"excerpt":{"rendered":"<p>Welcome to Research Focus, a new series of blog posts that highlights notable publications, events, code\/datasets, new hires and other milestones from across the research community at Microsoft. Barun Patra, Saksham Singhal, Shaohan Huang, Zewen Chi, Li Dong, Furu Wei, Vishrav Chaudhary, Xia Song The most recent addition to Microsoft&#8217;s Turing Universal Language Representation Model [&hellip;]<\/p>\n","protected":false},"author":42183,"featured_media":896202,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","footnotes":""},"categories":[1],"tags":[],"research-area":[13556,13545],"msr-region":[],"msr-event-type":[],"msr-locale":[268875],"msr-post-option":[243984],"msr-impact-theme":[],"msr-promo-type":[],"msr-podcast-series":[],"class_list":["post-895428","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-research-blog","msr-research-area-artificial-intelligence","msr-research-area-human-language-technologies","msr-locale-en_us","msr-post-option-blog-homepage-featured"],"msr_event_details":{"start":"","end":"","location":""},"podcast_url":"","podcast_episode":"","msr_research_lab":[],"msr_impact_theme":[],"related-publications":[],"related-downloads":[],"related-videos":[],"related-academic-programs":[],"related-groups":[144735,144812,144931,867219],"related-projects":[890049,691494,678390,649749,241727],"related-events":[],"related-researchers":[],"msr_type":"Post","featured_image_thumbnail":"<img width=\"960\" height=\"540\" src=\"https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2022\/11\/1400x788_Research_focus3_blog_thumbnail-scaled-960x540.jpg\" class=\"img-object-cover\" alt=\"Microsoft Research Focus 03: Week of November 7th, 2022\" decoding=\"async\" loading=\"lazy\" srcset=\"https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2022\/11\/1400x788_Research_focus3_blog_thumbnail-scaled-960x540.jpg 960w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2022\/11\/1400x788_Research_focus3_blog_thumbnail-300x169.jpg 300w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2022\/11\/1400x788_Research_focus3_blog_thumbnail-1024x576.jpg 1024w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2022\/11\/1400x788_Research_focus3_blog_thumbnail-768x432.jpg 768w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2022\/11\/1400x788_Research_focus3_blog_thumbnail-1536x864.jpg 1536w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2022\/11\/1400x788_Research_focus3_blog_thumbnail-2048x1153.jpg 2048w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2022\/11\/1400x788_Research_focus3_blog_thumbnail-1066x600.jpg 1066w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2022\/11\/1400x788_Research_focus3_blog_thumbnail-655x368.jpg 655w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2022\/11\/1400x788_Research_focus3_blog_thumbnail-343x193.jpg 343w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2022\/11\/1400x788_Research_focus3_blog_thumbnail-240x135.jpg 240w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2022\/11\/1400x788_Research_focus3_blog_thumbnail-scaled-640x360.jpg 640w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2022\/11\/1400x788_Research_focus3_blog_thumbnail-1280x720.jpg 1280w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2022\/11\/1400x788_Research_focus3_blog_thumbnail-1920x1080.jpg 1920w\" sizes=\"(max-width: 960px) 100vw, 960px\" \/>","byline":"","formattedDate":"November 8, 2022","formattedExcerpt":"Welcome to Research Focus, a new series of blog posts that highlights notable publications, events, code\/datasets, new hires and other milestones from across the research community at Microsoft. Barun Patra, Saksham Singhal, Shaohan Huang, Zewen Chi, Li Dong, Furu Wei, Vishrav Chaudhary, Xia Song The&hellip;","locale":{"slug":"en_us","name":"English","native":"","english":"English"},"_links":{"self":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/posts\/895428"}],"collection":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/users\/42183"}],"replies":[{"embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/comments?post=895428"}],"version-history":[{"count":28,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/posts\/895428\/revisions"}],"predecessor-version":[{"id":898356,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/posts\/895428\/revisions\/898356"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/media\/896202"}],"wp:attachment":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/media?parent=895428"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/categories?post=895428"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/tags?post=895428"},{"taxonomy":"msr-research-area","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/research-area?post=895428"},{"taxonomy":"msr-region","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-region?post=895428"},{"taxonomy":"msr-event-type","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-event-type?post=895428"},{"taxonomy":"msr-locale","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-locale?post=895428"},{"taxonomy":"msr-post-option","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-post-option?post=895428"},{"taxonomy":"msr-impact-theme","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-impact-theme?post=895428"},{"taxonomy":"msr-promo-type","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-promo-type?post=895428"},{"taxonomy":"msr-podcast-series","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-podcast-series?post=895428"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}